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Brown S, Sinha S, Schaefer AJ. Markov decision process design: A framework for integrating strategic and operational decisions. OPERATIONS RESEARCH LETTERS 2024; 54:107090. [PMID: 38560724 PMCID: PMC10979703 DOI: 10.1016/j.orl.2024.107090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates the design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem setting arises naturally in several application areas, as we illustrate through examples. We derive a bilevel mixed-integer linear programming formulation for the problem and perform a computational study to demonstrate that realistic instances can be solved numerically.
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Affiliation(s)
- Seth Brown
- Computational Applied Mathematics & Operations Research, Rice University, 6100 Main St, Houston, 77005, TX, USA
| | - Saumya Sinha
- Computational Applied Mathematics & Operations Research, Rice University, 6100 Main St, Houston, 77005, TX, USA
| | - Andrew J. Schaefer
- Computational Applied Mathematics & Operations Research, Rice University, 6100 Main St, Houston, 77005, TX, USA
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2
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Richard D, Jang J, Çıtmacı B, Luo J, Canuso V, Korambath P, Morales-Leslie O, Davis JF, Malkani H, Christofides PD, Morales-Guio CG. Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems. iScience 2023; 26:106966. [PMID: 37378322 PMCID: PMC10291476 DOI: 10.1016/j.isci.2023.106966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023] Open
Abstract
As renewable electricity becomes cost competitive with fossil fuel energy sources and environmental concerns increase, the transition to electrified chemical and fuel synthesis pathways becomes increasingly desirable. However, electrochemical systems have traditionally taken many decades to reach commercial scales. Difficulty in scaling up electrochemical synthesis processes comes primarily from difficulty in decoupling and controlling simultaneously the effects of intrinsic kinetics and charge, heat, and mass transport within electrochemical reactors. Tackling this issue efficiently requires a shift in research from an approach based on small datasets, to one where digitalization enables rapid collection and interpretation of large, well-parameterized datasets, using artificial intelligence (AI) and multi-scale modeling. In this perspective, we present an emerging research approach that is inspired by smart manufacturing (SM), to accelerate research, development, and scale-up of electrified chemical manufacturing processes. The value of this approach is demonstrated by its application toward the development of CO2 electrolyzers.
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Affiliation(s)
- Derek Richard
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Joonbaek Jang
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Berkay Çıtmacı
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Junwei Luo
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vito Canuso
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Prakashan Korambath
- Office of Advanced Research Computing, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Olivia Morales-Leslie
- Office of Advanced Research Computing, University of California, Los Angeles, Los Angeles, CA 90095, USA
- CESMII, Los Angeles, CA 90095, USA
| | - James F. Davis
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Office of Advanced Research Computing, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | | | - Panagiotis D. Christofides
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Carlos G. Morales-Guio
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
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3
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Production scheduling under demand uncertainty in the presence of feedback: Model comparisons, insights, and paradoxes. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Vasilas N, Papadopoulos AI, Papadopoulos L, Salamanis A, Kazepidis P, Soudris D, Kehagias D, Seferlis P. Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous infrastructures. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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5
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Liu Z, Li S, Ge Y. A parallel algorithm based on quantum annealing and double-elite spiral search for mixed-integer optimal control problems in engineering. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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6
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Andrés‐Martínez O, Ricardez‐Sandoval LA. Integration of planning, scheduling, and control: A review and new perspectives. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Andrés‐Martínez O, Ricardez‐Sandoval LA. A nested online scheduling and nonlinear model predictive control framework for multi‐product continuous systems. AIChE J 2022. [DOI: 10.1002/aic.17665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Yamada M, Badr S, Udugama IA, Fukuda S, Nakaya M, Yoshioka Y, Sugiyama H. A systematic techno-economic approach to decide between continuous and batch operation modes for injectable manufacturing. Int J Pharm 2021; 613:121353. [PMID: 34896214 DOI: 10.1016/j.ijpharm.2021.121353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/08/2021] [Accepted: 12/02/2021] [Indexed: 12/23/2022]
Abstract
A comprehensive approach is proposed to systematically determine the optimal mode of operation between continuous and batch injectable manufacturing considering product and market conditions. At the core of this approach are two integrated complete mathematical modules for discrete and continuous injectable manufacturing, which are supplemented with an economic evaluation module that can then be used to explore the impact of all relevant process parameters (e.g., lot-size, number of operators, solubility, product demand, raw material costs). When the developed approach was applied to two case studies, it was found that batch production was preferred at low to moderate solution (raw material) costs. In contrast, at higher solution costs, the preference for batch and continuous production processes changed back and forth as the annual product demand changed. The study also found that continuous production processes became increasingly preferred at medium to large final dosage volumes and a competitive alternative even at moderate solution costs. From a decision-making point of view, batch injectable manufacturing will be preferred over the novel continuous manufacturing technology unless there is a significant economic incentive to overcome the perceived technology risk. The proposed approach is intended as a decision-support tool for pharmaceutical process engineers.
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Affiliation(s)
- Masahiro Yamada
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Sara Badr
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Isuru A Udugama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Shouko Fukuda
- Settsu Plant, Shionogi Pharma Co., Ltd., 2-5-1, Mishima, Settsu-Shi, 556-0022 Osaka, Japan
| | - Manabu Nakaya
- Settsu Plant, Shionogi Pharma Co., Ltd., 2-5-1, Mishima, Settsu-Shi, 556-0022 Osaka, Japan
| | - Yasuyuki Yoshioka
- Settsu Plant, Shionogi Pharma Co., Ltd., 2-5-1, Mishima, Settsu-Shi, 556-0022 Osaka, Japan
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan.
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Patilas CS, Kookos IK. Algorithmic Approach to the Simultaneous Design and Control Problem. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Christos S. Patilas
- Department of Chemical Engineering, University of Patras, Rio, Patras 26504, Greece
- Research Infrastructure for Waste Valorization and Sustainable Management of Resources, University of Patras, Rio, Patras 26504, Greece
| | - Ioannis K. Kookos
- Department of Chemical Engineering, University of Patras, Rio, Patras 26504, Greece
- Research Infrastructure for Waste Valorization and Sustainable Management of Resources, University of Patras, Rio, Patras 26504, Greece
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Williams B, Cremaschi S. Selection of surrogate modeling techniques for surface approximation and surrogate-based optimization. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.03.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Abstract
The application of white box models in digital twins is often hindered by missing knowledge, uncertain information and computational difficulties. Our aim was to overview the difficulties and challenges regarding the modelling aspects of digital twin applications and to explore the fields where surrogate models can be utilised advantageously. In this sense, the paper discusses what types of surrogate models are suitable for different practical problems as well as introduces the appropriate techniques for building and using these models. A number of examples of digital twin applications from both continuous processes and discrete manufacturing are presented to underline the potentials of utilising surrogate models. The surrogate models and model-building methods are categorised according to the area of applications. The importance of keeping these models up to date through their whole model life cycle is also highlighted. An industrial case study is also presented to demonstrate the applicability of the concept.
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Pistikopoulos EN, Tian Y, Bindlish R. Operability and control in process intensification and modular design: Challenges and opportunities. AIChE J 2021. [DOI: 10.1002/aic.17204] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
| | - Yuhe Tian
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
| | - Rahul Bindlish
- Engineering Solutions Technology Center, The Dow Chemical Company Texas USA
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Pappas I, Kenefake D, Burnak B, Avraamidou S, Ganesh HS, Katz J, Diangelakis NA, Pistikopoulos EN. Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward. FRONTIERS IN CHEMICAL ENGINEERING 2021. [DOI: 10.3389/fceng.2020.620168] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.
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Patilas CS, Kookos IK. A quadratic approximation of the back-off methodology for the control structure selection problem. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107114] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Katz J, Pistikopoulos EN. A partial multiparametric optimization strategy to improve the computational performance of model predictive control. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107057] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Rafiei M, Ricardez-Sandoval LA. Integration of design and control for industrial-scale applications under uncertainty: a trust region approach. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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Huesman A. Integration of operation and design of solar fuel plants: A carbon dioxide to methanol case study. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106836] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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de Carvalho RF, Alvarez LA. Simultaneous Process Design and Control of the Williams–Otto Reactor Using Infinite Horizon Model Predictive Control. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01953] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Romero F. de Carvalho
- School of Chemical Engineering, University of Campinas, UNICAMP, 13083-852, Campinas-SP, Brazil
| | - Luz A. Alvarez
- School of Chemical Engineering, University of Campinas, UNICAMP, 13083-852, Campinas-SP, Brazil
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21
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Burnak B, Pistikopoulos EN. Integrated process design, scheduling, and model predictive control of batch processes with closed‐loop implementation. AIChE J 2020. [DOI: 10.1002/aic.16981] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Baris Burnak
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute Texas A&M University College Station College Station Texas USA
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute Texas A&M University College Station College Station Texas USA
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Katz J, Pappas I, Avraamidou S, Pistikopoulos EN. Integrating deep learning models and multiparametric programming. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106801] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Onel M, Burnak B, Pistikopoulos EN. Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control. Ind Eng Chem Res 2020; 59:2291-2306. [PMID: 32549652 DOI: 10.1021/acs.iecr.9b04226] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model predictive control (mp-MPC). The strategy features (i) data-driven fault detection and diagnosis models by using the support vector machine (SVM) algorithm, (ii) ranking via a nonlinear, kernel-dependent, SVM-based feature selection algorithm, (iii) data-driven regression models for fault magnitude estimation via the random forest algorithm, and (iv) a parametric optimization and control (PAROC) framework for the design of the explicit/multiparametric model predictive controller. The resulting explicit control strategies correspond to affine functions of the system states and the magnitude of the detected fault. A semibatch process, an example for penicillin production, is presented to demonstrate how the proposed framework ensures smart operation for which rapid switches between a priori computed explicit control action strategies are enabled by continuous process monitoring information.
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Affiliation(s)
- Melis Onel
- † Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.,‡ Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
| | - Baris Burnak
- † Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.,‡ Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
| | - Efstratios N Pistikopoulos
- † Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States.,‡ Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
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Rafiei M, Ricardez‐Sandoval LA. A trust‐region framework for integration of design and control. AIChE J 2020. [DOI: 10.1002/aic.16922] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Mina Rafiei
- Department of Chemical EngineeringUniversity of Waterloo Waterloo Canada
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Rafiei M, Ricardez-Sandoval LA. New frontiers, challenges, and opportunities in integration of design and control for enterprise-wide sustainability. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106610] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Valdez-Navarro YI, Ricardez-Sandoval LA. A Novel Back-off Algorithm for Integration of Scheduling and Control of Batch Processes under Uncertainty. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04963] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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28
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Tsay C, Baldea M. 110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02282] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Calvin Tsay
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
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Towards the Grand Unification of Process Design, Scheduling, and Control—Utopia or Reality? Processes (Basel) 2019. [DOI: 10.3390/pr7070461] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
As a founder of the Process Systems Engineering (PSE) discipline, Professor Roger W.H. Sargent had set ambitious goals for a systematic new generation of a process design paradigm based on optimization techniques with the consideration of future uncertainties and operational decisions. In this paper, we present a historical perspective on the milestones in model-based design optimization techniques and the developed tools to solve the resulting complex problems. We examine the progress spanning more than five decades, from the early flexibility analysis and optimal process design under uncertainty to more recent developments on the simultaneous consideration of process design, scheduling, and control. This formidable target towards the grand unification poses unique challenges due to multiple time scales and conflicting objectives. Here, we review the recent progress and propose future research directions.
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